The present invention relates to medical instruments and systems for monitoring, displaying, controlling and interpreting data typically extracted from bodily fluid analytes of mammalians.
Diabetes mellitus (DM) is the common name for a series of metabolic disorders caused mainly by defects in the glucose regulatory system leading to a partial or total destruction of the insulin producing beta cells. Insulin resistance, insufficient amount or total loss of insulin, reduce or inhibit counter regulatory means to achieve glucose homeostasis. Impaired glucose regulation is reflected in elevated glucose levels and glucose fluctuations.
Elevated levels of glucose, hyperglycemia, gradually induce or increase the risk of developing diabetic micro- and macrovascular complications. Long term risk and complications increase approximately exponentially in relation to a glucose mean level. Microvascular complications include neuropathy (nerve damage), nephropathy (kidney disease) and vision disorders (e.g. retinopathy, glaucoma, cataract and corneal disease). Macrovascular complications include heart disease, stroke and peripheral vascular disease (which can lead to ulcers, gangrene and amputation). Additionally, other complications include infections, metabolic difficulties, impotence, autonomic neuropathy and pregnancy problems.
The objective of DM treatment is to continuously maintain the concentration of blood glucose close to normoglycemia. This means staying below hyperglycemic levels but above the critical concentration level at which the amount of glucose is not sufficient for vital energy supply, hypoglycemia. When the glucose concentration is too low, symptoms like unconsciousness, confusion or seizure that preclude self-treatment set in. Recurring severe hypoglycemic events increase the risk of brain damage.
For patients with diabetes, self-management is a lifelong struggle trying to achieve treatment targets while balancing between the short term risk of hypoglycemia and the risk of long-term medical complications due to hyperglycemia. The dynamics and complexity of DM with multiple influencing factors, in combination with insufficient options for treatment, make successful therapy a difficult challenge. For example, in Sweden, (known for its good health care), only approximately 50% of the diabetic patients reach treatment targets.
Apart from insulin regimens, DM treatment primarily relies upon behavioral dietary adjustments, lifestyle management and/or anti-diabetic drug therapy. The latter generally becomes necessitated as the disease progresses, and may be treated by a variety of drugs. The most common drugs are oral agents like metformin that increases sensitivity to insulin and sulfonylurea that stimulates the pancreas to produce more insulin. Insulin on the other hand acts on cells to stimulate uptake, utilization and storage of glucose.
Regardless of DM type, treatment targets and strategies are based on glucometer readings either from sparsely sampled finger pricking using self-monitoring of blood glucose (SMBG) or by densely sampled continuous glucose monitoring (CGM).
Using a glucometer is the most common method for quantifying the concentration of blood glucose. The frequency of measurements varies widely among individuals depending on type and progression of the disease, motivation, treatment regime and other circumstances. FBG (fasting blood glucose), pre-prandial (before meal) and post prandial (after meal) blood glucose measurements are practiced in modern intensive treatment of diabetes. In general, more daily measurements allow better control and less glucose variability.
In CGM a disposable glucose sensor is inserted subcutaneously and determines the blood glucose level continuously until sensor replacement is needed after a few days. While CGM has proven useful, its use is still limited, partly due to high costs, accuracy and reliability issues. In addition, CGM technology forms the basis for closed loop artificial pancreas research and involves connecting an insulin pump to the CGM using a controller.
Presentation of glycemic data can be found in hand-held measuring devices, medical device displays and diabetes software for computers, smart phones etc. Many glucometers only display blood glucose values as discrete digits while some graphically display glucose levels that enable monitoring of blood glucose changes and trends over time. Advanced glucometers, CGM and software for diabetes data analysis sometimes display data in one-dimensional bar-graphs or thermometer-type displays or two-dimensional diagrams where the Y-axis represent a glucose-level with a linear range and the X-axis represent time. Moreover, standard statistical measures including the arithmetic mean value, standard deviation, coefficient of variation, min and max values are usually applied on glucose readings over different time ranges and time segments in diabetes software. The glucose readings are the only feedback available to the user to assess and evaluate the effects of treatment regimens.
Despite pharmaceutical and technical advances in treatment of diabetes the means to reach satisfactory blood glucose levels are neither adequate nor sufficient. The complexity of glucose dynamics necessitates the patient to develop an understanding of causality in order to act proactive instead of reactive. Treatment feedback from typical instruments and tools exhibit serious limitations that hampers understanding of the problems involved in the control of diabetes.
Current tools and methods used for presentation and interpretation of blood glucose data are based on common statistical methods that assume that the sampled glucose data is normally (Gaussian) distributed. Normally distributed data provides a probability distribution that has the characteristic bell shape that decreases symmetrically on both sides of the mean peak.
In contrast, some researchers, [U.S. application Ser. No. 10/240,228, Kovatchev and Cox] have incorrectly suggested that the distribution of blood glucose follows a log-normal distribution. In addition, in their proposed method, the glycemic identity in mmol/l or mg/dl is lost and a surrogate value is used. As of today, there are no commercially available instruments or tools based on the assumption of glucose data being log-normally distributed or any other distribution, besides the normal Gaussian distribution.
The research behind the invention indicates that the concentration and dynamics of blood glucose are affected by biological constrains, various complex interactions and non-linear biological control mechanisms. Therefore, blood glucose and its measurements, generally is neither normally nor log-normally distributed. Thus, presentation, indication and statistics of glucose data are often biased and therefore impairing interpretation, treatment and potential feedback to the observer.
The glucose concentration exhibits a lower boundary, which implies that this concentration never drops to zero. Biologically, there are safety mechanisms like hormone signaling and glucose release that strives to supply vital organs with essential amounts of energy for survival. At the upper end of the scale another protective mechanism, the renal threshold, restricts cellular damage and acute ketoacidosis from elevated glucose levels. This inter-individually variable threshold triggers the kidneys to release excess glucose into the urine in relation to glucose level in gradually increasing amounts.
The above described boundaries are general in their mechanism. However, the concentration level at which they emerge and the resulting metabolic impact varies substantially between individuals. Importantly but entirely overlooked is the fact that every individual has a unique glucose probability distribution which in addition changes over time. Its shape and asymmetry are vastly affected by DM type, DM stage, glucose control and treatment regimen, see FIGS. 1 and 2. Consequently, current methods, instruments and tools do not take this into account and typically suffer from incorrect bias.
Presentation and Graphical Interpretation
Currently used means for the presentation and display of glucose data in self-management of diabetes or clinical instruments utilize a linear presentation scale. Typical instruments are glucometers, CGM and various computer software tools. The presentation and display of glucose information are neither adapted to, nor corrected for the unique physiological state, glucose dynamics and glucose statistics that characterize an individual diabetic patient, or patient population. The typical cluster of glucose readings will generally not be symmetrically distributed around the mean—making interpretation of changes in glucose levels difficult and sometimes misleading or obscured for the observer. Additionally, a universal presentation scale based on the assumption of a typical distribution (normal or even log-normal), will for many patients suppress the resolution in important areas of the blood glucose range such as in the hypo- or hyperglycemic regions, thus obscuring potential risk assessment.
Real Time Glucose Monitoring and Related Methods
A subarea of glucose monitoring focuses on real-time measurements. This mainly involves a continuous glucose monitoring device (CGM), or in combination with an insulin administration pump, forming an artificial pancreas. Real-time monitoring of changes in glucose concentration usually consists of rate of change indicators and predictive alarms. For adequate performance such features necessitate some kind of linearization of the glucose propagation over time. This precondition is generally not fulfilled.
Rate of change is presented in some instruments by an arrow where the tilt angle reflects the velocity and estimated risk of the glucose change. As the detector driving the tilt angle of the arrow usually does not take into account the non-linear glucose propagation, the indication is often misleading. Thus such indicators fail to properly demonstrate the magnitude of the risk posed by a certain glucose level change.
Furthermore, the non-linear glucose propagation impairs the accuracy and reliability of typical alarm prediction algorithms. This results in unnecessitated and irrelevant alarms and indications in the hyperglycemic range, and too few alarms and too small indications in the hypoglycemic range for certain types of DM patients. Thus, the true clinical value of this feature has been somewhat limited.
Statistical Measures
Statistical analyses, for example estimation of glucose mean values and glucose variability is a typical feature within more advanced glucometers, CGM and computer software tools. The estimation of average glucose levels is fundamental in diagnosis, classification, self-care and treatment. The normal practice of estimating the average glucose value is by the use of the arithmetic mean. For asymmetric glucose distributions this renders unreliable results. Further, the variability measures, i.e. the standard deviation or coefficient of variation, are affected by both the properties of the glucose distribution and the way the mean value was obtained. From a treatment perspective, the disadvantages of these standard measures imply a reduced accuracy in diagnosis, improper interpretation and inaccurate results.
In conclusion, user feedback from measurements, diagnosis, analysis, treatment and self-care in the field of diabetes has since its inception been plagued with problems originating from the assumption that blood glucose data is normally distributed (or by some, logarithmically distributed). Unfortunately, this applies to everything from clinical lab equipment and self-management devices for glucose measurements, to results and statistics presented in clinical studies and scientific research.